3,530 research outputs found
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
We propose two strategies to improve the quality of tractography results
computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both
methods are based on the same PDE framework, defined in the coupled space of
positions and orientations, associated with a stochastic process describing the
enhancement of elongated structures while preserving crossing structures. In
the first method we use the enhancement PDE for contextual regularization of a
fiber orientation distribution (FOD) that is obtained on individual voxels from
high angular resolution diffusion imaging (HARDI) data via constrained
spherical deconvolution (CSD). Thereby we improve the FOD as input for
subsequent tractography. Secondly, we introduce the fiber to bundle coherence
(FBC), a measure for quantification of fiber alignment. The FBC is computed
from a tractography result using the same PDE framework and provides a
criterion for removing the spurious fibers. We validate the proposed
combination of CSD and enhancement on phantom data and on human data, acquired
with different scanning protocols. On the phantom data we find that PDE
enhancements improve both local metrics and global metrics of tractography
results, compared to CSD without enhancements. On the human data we show that
the enhancements allow for a better reconstruction of crossing fiber bundles
and they reduce the variability of the tractography output with respect to the
acquisition parameters. Finally, we show that both the enhancement of the FODs
and the use of the FBC measure on the tractography improve the stability with
respect to different stochastic realizations of probabilistic tractography.
This is shown in a clinical application: the reconstruction of the optic
radiation for epilepsy surgery planning
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding
Reducing the radiation dose in computed tomography (CT) is important to
mitigate radiation-induced risks. One option is to employ a well-trained model
to compensate for incomplete information and map sparse-view measurements to
the CT reconstruction. However, reconstruction from sparsely sampled
measurements is insufficient to uniquely characterize an object in CT, and a
learned prior model may be inadequate for unencountered cases. Medical modal
translation from magnetic resonance imaging (MRI) to CT is an alternative but
may introduce incorrect information into the synthesized CT images in addition
to the fact that there exists no explicit transformation describing their
relationship. To address these issues, we propose a novel framework called the
denoising diffusion model for medical image synthesis (DDMM-Synth) to close the
performance gaps described above. This framework combines an MRI-guided
diffusion model with a new CT measurement embedding reverse sampling scheme.
Specifically, the null-space content of the one-step denoising result is
refined by the MRI-guided data distribution prior, and its range-space
component derived from an explicit operator matrix and the sparse-view CT
measurements is directly integrated into the inference stage. DDMM-Synth can
adjust the projection number of CT a posteriori for a particular clinical
application and its modified version can even improve the results significantly
for noisy cases. Our results show that DDMM-Synth outperforms other
state-of-the-art supervised-learning-based baselines under fair experimental
conditions.Comment: llncs.cls v2.20,12 pages with 6 figure
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence
In this article, we demonstrate the use of artificial neural networks as
optimal maps which are utilized for convolution and deconvolution of
coarse-grained fields to account for sub-grid scale turbulence effects. We
demonstrate that an effective eddy-viscosity is predicted by our purely
data-driven large eddy simulation framework without explicit utilization of
phenomenological arguments. In addition, our data-driven framework precludes
the knowledge of true sub-grid stress information during the training phase due
to its focus on estimating an effective filter and its inverse so that
grid-resolved variables may be related to direct numerical simulation data
statistically. The proposed predictive framework is also combined with a
statistical truncation mechanism for ensuring numerical realizability in an
explicit formulation. Through this we seek to unite structural and functional
modeling strategies for modeling non-linear partial differential equations
using reduced degrees of freedom. Both a priori and a posteriori results are
shown for a two-dimensional decaying turbulence case in addition to a detailed
description of validation and testing. A hyperparameter sensitivity study also
shows that the proposed dual network framework simplifies learning complexity
and is viable with exceedingly simple network architectures. Our findings
indicate that the proposed framework approximates a robust and stable sub-grid
closure which compares favorably to the Smagorinsky and Leith hypotheses for
capturing the theoretical scaling in Kraichnan turbulence
NIO: Lightweight neural operator-based architecture for video frame interpolation
We present, NIO - Neural Interpolation Operator, a lightweight efficient
neural operator-based architecture to perform video frame interpolation.
Current deep learning based methods rely on local convolutions for feature
learning and require a large amount of training on comprehensive datasets.
Furthermore, transformer-based architectures are large and need dedicated GPUs
for training. On the other hand, NIO, our neural operator-based approach learns
the features in the frames by translating the image matrix into the Fourier
space by using Fast Fourier Transform (FFT). The model performs global
convolution, making it discretization invariant. We show that NIO can produce
visually-smooth and accurate results and converges in fewer epochs than
state-of-the-art approaches. To evaluate the visual quality of our interpolated
frames, we calculate the structural similarity index (SSIM) and Peak Signal to
Noise Ratio (PSNR) between the generated frame and the ground truth frame. We
provide the quantitative performance of our model on Vimeo-90K dataset, DAVIS,
UCF101 and DISFA+ dataset
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